no code implementations • 26 May 2022 • Ümit V. Çatalyürek, Karen D. Devine, Marcelo Fonseca Faraj, Lars Gottesbüren, Tobias Heuer, Henning Meyerhenke, Peter Sanders, Sebastian Schlag, Christian Schulz, Daniel Seemaier, Dorothea Wagner
In recent years, significant advances have been made in the design and evaluation of balanced (hyper)graph partitioning algorithms.
2 code implementations • 26 Mar 2020 • Lars Gottesbüren, Michael Hamann, Sebastian Schlag, Dorothea Wagner
We present an improvement to the flow-based refinement framework of KaHyPar-MF, the current state-of-the-art multilevel $k$-way hypergraph partitioning algorithm for high-quality solutions.
Data Structures and Algorithms
1 code implementation • 3 Jul 2019 • Lars Gottesbüren, Michael Hamann, Dorothea Wagner
The solution quality is only marginally worse than that of the best-performing hypergraph partitioners KaHyPar and hMETIS, while being one order of magnitude faster.
Data Structures and Algorithms
1 code implementation • 27 Jun 2019 • Lars Gottesbüren, Michael Hamann, Tim Niklas Uhl, Dorothea Wagner
Graph partitioning has many applications.
Data Structures and Algorithms
4 code implementations • 11 Jun 2019 • Moritz Baum, Valentin Buchhold, Jonas Sauer, Dorothea Wagner, Tobias Zündorf
We study a multimodal journey planning scenario consisting of a public transit network and a transfer graph which represents a secondary transportation mode (e. g., walking, cycling, e-scooter).
Data Structures and Algorithms
4 code implementations • 17 Mar 2017 • Julian Dibbelt, Thomas Pajor, Ben Strasser, Dorothea Wagner
The output consist is a sequence of vehicles such as trains or buses that a traveler should take to get from the source to the target.
Data Structures and Algorithms
no code implementations • 20 Apr 2015 • Hannah Bast, Daniel Delling, Andrew Goldberg, Matthias Müller-Hannemann, Thomas Pajor, Peter Sanders, Dorothea Wagner, Renato F. Werneck
We survey recent advances in algorithms for route planning in transportation networks.
Data Structures and Algorithms G.2.1; G.2.2; G.2.3; H.2.8; H.3.5; H.4.2
no code implementations • 3 Feb 2014 • Julian Dibbelt, Ben Strasser, Dorothea Wagner
We consider the problem of quickly computing shortest paths in weighted graphs given auxiliary data derived in an expensive preprocessing phase.